<p>We start by slightly modifying the generic framework for solving online covering and packing linear programs (LP) proposed in the seminal work of Buchbinder and Naor (Mathematics of Operations Research, 34, 2009) to obtain efficient implementations in settings in which one has access to a separation oracle.</p> <p><br></p> <p>We then apply the generic framework to several online network connectivity problems with LP formulations, namely pairwise spanners and directed Steiner forests. Our results are comparable to the previous state-of-the-art results for these problems in the offline setting.</p> <p><br></p> <p>Further, we extend the generic frameworks to online optimization problems enhanced with <strong>machine-learning predictions</strong>. In particular, we present <strong>learning-augmented</strong> algorithms for online covering LPs and semidefinite programs (SDP), which outperform any optimal online algorithms when the prediction is accurate while maintaining reasonable guarantees when the prediction is misleading. Specifically, we obtain general online learning-augmented algorithms for covering LPs with fractional advice and general constraints and initiate the study of learning-augmented algorithms for covering SDPs.</p>

<div>Touch event detection investigates the interaction between two people from video recordings. We are interested in a particular type of interaction which occurs between a caregiver and an infant, as touch is a key social and emotional signal used by caregivers when interacting with their children. We propose an automatic touch event detection and recognition method to determine the potential timing when the caregiver touches the infant, and classify the event into six touch types based on which body part of the infant has been touched. We leverage deep learning based human pose estimation and person segmentation to analyze the spatial relationship between the caregivers’ hands and the infant. We demonstrate promising performance on touch event detection and classification, showing great potential for reducing human effort when generating groundtruth annotation.</div><div><br></div><div>Recently, artificial intelligence powered techniques have shown great potential to increase the efficiency of video compression. In this thesis, we describe a texture analysis pre-processing method that leverages deep learning based scene understanding to extract semantic areas for the improvement of subsequent video coder. Our proposed method generates a pixel-level texture mask by combining the semantic segmentation with simple post-processing strategy. Our approach is integrated into a switchable texture-based video coding method. We demonstrate that for many standard and user generated test sequences, the proposed method achieves significant data rate reduction without noticeable visual artifacts.</div>

Denna rapport kommer fokusera på att beskriva processen på den fortsatta utvecklingen av det grafiska användargränssnittet (GUI) för Hanabi Benchmark. Hanabi är ett kortspel som introducerats som ett nytt forskningsområde inom artificiell intelligens (AI). Målet med projektet var att implementera en mänsklig användare, som sedan skulle kunna spela med maskin lärlingsbaserade agenter med andra ord icke-mänskliga spelare genom GUI.För att uppnå målen, implementerade vi kontroller för den mänskliga användaren i GUI. Modeller av agenter integrerades in till GUI som mänskliga användaren sedan skulle spela med. Slutligen utfördes en användarstudie för att utvärdera de olika agenternas prestation. / This report will describe the further development of the Graphical User Interface (GUI) for the Hanabi Benchmark. Hanabi is a card game that has been introduced as a new frontier for artificial intelligence (AI). The goal of the project was to implement a human-user, into the GUI, and give the possibility to play against Machine Learning (ML) based agents, viz, non-human players in the GUI.To achieve these goals, we implemented human controls into the GUI to give a human user the option to play the game in the GUI. Agent models were integrated into to the GUI for the human to play with. Finally, a small study was conducted to evaluate the agent’s performances.

<p>In the last decade, people have been surrounded by mobile devices such as smartphones, smartwatches, laptops, smart TVs, tablets, and IoT devices. As sensitive personal information such as photos, messages, contact information, schedules, and bank accounts are all stored on mobile devices today, the security and protection of such personal information are becoming more and more important. Today’s mobile devices are equipped with a variety of embedded sensors such as accelerometer, gyroscope, magnetometer, camera, GPS sensor, acoustic sensors, etc. that produce raw data on location, motion, and the environment around us. Based on these sensor data, we propose novel in-air signature authentication technologies on both smartphone and smartwatch in this dissertation. In-air signature authentication, as an essential behavioral biometric trait, has been adopted for identity verification and user authorization, as well as the development of deep neural networks, has vastly facilitated this field. This dissertation examines two challenging problems. One problem is how to deploy machine learning techniques to authenticate user in-air signatures in more convenient, intuitive, and secure ways by using smartphone and smartwatch in daily settings. Another problem is how to deal with the limited computational resources on today’s mobile devices which restrict to use machine learning models due to the substantial computational costs introduced by millions of parameters. </p> <p>To address the two above problems separately, we conduct the following research works. 1) The first work AirSign leverages both in-built acoustic and motion sensors on today’s smartphone for user authentication by signing signatures in the air without requiring any special hardware. This system actively transmits inaudible acoustic signals from the earpiece speaker, receives echoes back through both in-built microphones to “illuminate” signature and hand geometry, and authenticates users according to the unique features extracted from echoes and motion sensors. 2) The second work DeepWatchSign leverages in-built motion sensors on today’s smartwatch for user in-air signature authentication. The system adopts LSTM-AutoEncoder to generate negative signature data automatically from the enrolled signatures and authenticates each user by the deep neural network model. 3) We close this dissertation with an l0-based sparse group lasso approach called MobilePrune which can compress the deep learning models for both desktop and mobile platforms. This approach adopts group lasso penalty to enforce sparsity at the group level to benefit General Matrix Multiply (GEMM) and optimize the l0 norm in an exact manner. We observe the substantial reduction of compression ratio and computational costs for deep learning models. This method also achieves less response delay and battery consumption on mobile devices.</p>

<p> Roller bearing is one of the machine industry’s common components. The roller bearing operation status is usually related to production efficiency. Failure of bearings during operation will cause downtime and severe economic losses. To prevent this situation, the proposal of effective bearing fault diagnosis methods has become a popular research topic. This thesis research first validates several popular bearing diagnosis methods based on signal processing and machine learning. Second, a novel signal feature extraction method called sparse wavelet packet transform (WPT) decomposition and a corresponding feature learning model called multi-scale and multi-rate convolutional neural network (MSMR-CNN) are proposed. Finally, the proposed method is verified using both Case Western Reserve University (CWRU) dataset and the self-collected dataset. The results demonstrate that our proposed MSMR-CNN method achieves higher performance of bearing fault classification accuracy in comparison with the methods which are recently proposed by the other researchers using machine learning and neural networks .</p>

This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets.

<p>Energy efficient machine learning accelerator design</p>

Connected and Automated Vehicles (CAVs) have the potential to revolutionize transportation efficiency and safety through collaborative behavior. This dissertation explores the challenges and opportunities associated with achieving socially optimal cooperative maneuvers, using the problem of cooperative lane-changing to showcase the significance of cooperativeness. Cooperative lane-changing serves as an ideal testbed for examining decentralized optimal control, interactions with uncooperative vehicles, accommodating diverse human driving preferences, and integrating planning and execution processes. Initially, the research focuses on scenarios where all vehicles are cooperative CAVs, leveraging their communication and coordination capabilities. Decentralized optimal control problems are formulated to minimize energy consumption, travel time, and traffic disruption during sequential cooperative lane changes, balancing individual vehicle objectives with system-level goals. The dissertation then extends the analysis to mixed-traffic scenarios involving uncooperative human-driven vehicles (HDVs). A novel approach is developed to ensure safety assurance, combining optimal control with Control Barrier Functions (CBFs) and fixed-time convergence (FxT-OCBF). Robust methods for handling disturbances from uncooperative vehicles are introduced, enhancing the resilience and dependability of cooperative lane-changing maneuvers. An innovative online learning framework is presented to address the complexities of CAVs interacting with HDVs exhibiting diverse driving preferences. Safety preferences are characterized using parameterized CBFs, and an extended Kalman filter dynamically adjusts control parameters based on observed interactions, enabling real-time adaptation to evolving human behaviors. The proposed methodologies bridge the gap between high-level planning and low-level control execution, facilitating safe and near-optimal cooperative maneuvers. Comprehensive analysis demonstrates improved traffic throughput, reduced energy consumption, and enhanced safety compared to non-cooperative or reactive approaches. This research lays the foundation for deploying CAV technologies that prioritize social optimality while addressing uncertainties in mixed-traffic settings, ultimately paving the way for safer and more efficient transportation systems. / 2025-03-11T00:00:00Z

O aprendizado semi-supervisionado é um paradigma do aprendizado de máquina no qual a hipótese é induzida aproveitando tanto os dados rotulados quantos os dados não rotulados. Este paradigma é particularmente útil quando a quantidade de exemplos rotulados é muito pequena e a rotulação manual dos exemplos é uma tarefa muito custosa. Nesse contexto, foi proposto o algoritmo Cotraining, que é um algoritmo muito utilizado no cenário semi-supervisionado, especialmente quando existe mais de uma visão dos dados. Esta característica do algoritmo Cotraining faz com que a sua aplicabilidade seja restrita a domínios multi-visão, o que diminui muito o potencial do algoritmo para resolver problemas reais. Nesta dissertação, é proposto o algoritmo Co2KNN, que é uma versão mono-visão do algoritmo Cotraining na qual, ao invés de combinar duas visões dos dados, combina duas estratégias diferentes de induzir classificadores utilizando a mesma visão dos dados. Tais estratégias são chamados de k-vizinhos mais próximos (KNN) Local e Global. No KNN Global, a vizinhança utilizada para predizer o rótulo de um exemplo não rotulado é conformada por aqueles exemplos que contém o novo exemplo entre os seus k vizinhos mais próximos. Entretanto, o KNN Local considera a estratégia tradicional do KNN para recuperar a vizinhança de um novo exemplo. A teoria do Aprendizado Semi-supervisionado Baseado em Desacordo foi utilizada para definir a base teórica do algoritmo Co2KNN, pois argumenta que para o sucesso do algoritmo Cotraining, é suficiente que os classificadores mantenham um grau de desacordo que permita o processo de aprendizado conjunto. Para avaliar o desempenho do Co2KNN, foram executados diversos experimentos que sugerem que o algoritmo Co2KNN tem melhor performance que diferentes algoritmos do estado da arte, especificamente, em domínios mono-visão. Adicionalmente, foi proposto um algoritmo otimizado para diminuir a complexidade computacional do KNN Global, permitindo o uso do Co2KNN em problemas reais de classificação / Semi-supervised learning is a machine learning paradigm in which the induced hypothesis is improved by taking advantage of unlabeled data. Semi-supervised learning is particularly useful when labeled data is scarce and difficult to obtain. In this context, the Cotraining algorithm was proposed. Cotraining is a widely used semisupervised approach that assumes the availability of two independent views of the data. In most real world scenarios, the multi-view assumption is highly restrictive, impairing its usability for classifification purposes. In this work, we propose the Co2KNN algorithm, which is a one-view Cotraining approach that combines two different k-Nearest Neighbors (KNN) strategies referred to as global and local k-Nearest Neighbors. In the global KNN, the nearest neighbors used to classify a new instance are given by the set of training examples which contains this instance within its k-nearest neighbors. In the local KNN, on the other hand, the neighborhood considered to classify a new instance is the set of training examples computed by the traditional KNN approach. The Co2KNN algorithm is based on the theoretical background given by the Semi-supervised Learning by Disagreement, which claims that the success of the combination of two classifiers in the Cotraining framework is due to the disagreement between the classifiers. We carried out experiments showing that Co2KNN improves significatively the classification accuracy specially when just one view of training data is available. Moreover, we present an optimized algorithm to cope with time complexity of computing the global KNN, allowing Co2KNN to tackle real classification problems

Num cenário em que dispositivos de aquisição de imagens e vídeo possuem recursos limitados ou as imagens disponíveis não possuem boa qualidade, as técnicas de super-resolução (SR) apresentam uma excelente alternativa para melhorar a qualidade das imagens. Nesta tese é apresentada uma proposta para super-resolução de imagem única que combina os benefícios da interpolação no domínio da transformada DCT e a eficiência dos métodos de reconstrução baseados no conceito de representação esparsa de sinais. A proposta busca aproveitar as melhorias já alcançadas na qualidade e eficiência computacional dos principais algoritmos de super-resolução existentes. O método de super-resolução proposto implementa algumas melhorias nas etapas de treinamento e reconstrução da imagem final. Na etapa de treinamento foi incluída uma nova etapa de extração de características utilizando técnicas de aguçamento por máscara de nitidez e construção de um novo dicionário. Esta estratégia busca extrair mais informações estruturais dos fragmentos de baixa e alta resolução do conjunto de treinamento e ao mesmo tempo reduzir o tamanho dos dicionários. Outra importante contribuição foi a inclusão de um processo iterativo e escalonável no algoritmo, reinserindo no conjunto de treinamento e na etapa de reconstrução, uma imagem de alta resolução obtida numa primeira iteração. Esta solução possibilitou uma melhora na qualidade da imagem de alta resolução final utilizando poucas imagens no conjunto de treinamento. As simulações computacionais demonstraram a capacidade do método proposto em produzir imagens com qualidade e com tempo computacional reduzido. / In a scenario in which the acquisition systems have limited resources or available images do not have good quality, the super-resolution (SR) techniques have become an excellent alternative for improving the image quality. In this thesis, we propose a single-image super-resolution (SR) method that combines the benefits of the DCT interpolation and efficiency of sparse representation method for image reconstruction. Also, the proposed method seeks to take advantage of the improvements already achieved in quality and computational efficiency of the existing SR algorithms. The proposed method implements some improvements in the dictionary training and the reconstruction process. A new dictionary was built by using an unsharp mask technique to characteristics extraction. Simultaneously, this strategy aim to extract more structural information of the low resolution and high resolution patches and reduce the dictionaries size. Another important contribution was the inclusion of an iterative and scalable process by reinserting the HR image obtained of first iteration. This solution aim to improve the quality of the final HR image using a few images in the training set. The results have demonstrated the ability of the proposed method to produce high quality images with reduced computational time.